comparison COBRAxy/src/flux_simulation.py @ 548:5aef7b860706 draft default tip

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547:73f2f7e2be17

        try:
            model_input.reactions.get_by_id(rxn_index).lower_bound = row.lower_bound
            model_input.reactions.get_by_id(rxn_index).upper_bound = row.upper_bound
        except KeyError:
            warning(f"Warning: Reaction {rxn_index} not found in model. Skipping.")
    
    return perform_sampling_and_analysis(model_input, cell_name)


def perform_sampling_and_analysis(model_path: str, cell_name: str) -> List[pd.DataFrame]:
    """
    Common function to perform sampling and analysis on a prepared model.

    Args:
        model_path (str): Path to the tabular model file. model with bounds applied.

    Returns:
        List[pd.DataFrame]: A list of DataFrames containing statistics and analysis results.
    """

    returnList = []

    model_input = model_utils.build_cobra_model_from_csv(model_path)

    if ARGS.sampling_enabled == "true":
    
        if ARGS.algorithm == 'OPTGP':
            OPTGP_sampler(model_input, cell_name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed)

548:5aef7b860706

        try:
            model_input.reactions.get_by_id(rxn_index).lower_bound = row.lower_bound
            model_input.reactions.get_by_id(rxn_index).upper_bound = row.upper_bound
        except KeyError:
            warning(f"Warning: Reaction {rxn_index} not found in model. Skipping.")

    return perform_sampling_and_analysis(None, cell_name, model_input=model_input)


def perform_sampling_and_analysis(model_path: str, cell_name: str, model_input: cobra.Model=None) -> List[pd.DataFrame]:
    """
    Common function to perform sampling and analysis on a prepared model.

    Args:
        model_path (str): Path to the tabular model file. model with bounds applied.

    Returns:
        List[pd.DataFrame]: A list of DataFrames containing statistics and analysis results.
    """

    returnList = []
    
    if model_input is None:
        model_input = model_utils.build_cobra_model_from_csv(model_path)

    if ARGS.sampling_enabled == "true":
    
        if ARGS.algorithm == 'OPTGP':
            OPTGP_sampler(model_input, cell_name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed)